This document summarizes several research papers on recommendation systems for web services. It discusses using rating data from users to recommend web services based on quality of service. Approaches include collaborative filtering to find similar users and services. Dynamic features are designed to describe user preferences and recommendations are made by weighting these features. The document also discusses using content-based filtering and social network data to provide recommendations. Improving recommendation diversity and combining collaborative and content-based filtering is addressed. Experimental results on real world datasets show hybrid approaches can improve performance on metrics like diversity, relevance and quality of service.
Structural Balance Theory Based Recommendation for Social Service PortalYogeshIJTSRD
There is enormous data present in our world. Therefore in order to access the most accurate information is becoming more difficult and complicated. As a result many relevant information gets missed which leads to much duplication of work and effort. Due to the huge search results, the user will generally have difficulty in identifying the relevant ones. To solve this problem, a recommendation system is used. A recommendation system is nothing but a filtering information system, which is used to predict the relevance of retrieved information according to the user’s needs for some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide huge records about any requested item or service. A proper recommendation system is used to separate this information result. A recommendation system can be improved further if supported with a level of trust information. That is, recommendations are prioritized according to their level of trust. Recommending appropriate needs social service to the target volunteers will become the key to ensure continuous success of social service. Today, many social service systems does not adopt any recommendation techniques. They provide advertisement or highlights request for a small commission. G. Banupriya | M. Anand "Structural Balance Theory-Based Recommendation for Social Service Portal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41216.pdf Paper URL: https://www.ijtsrd.comengineering/software-engineering/41216/structural-balance-theorybased-recommendation-for-social-service-portal/g-banupriya
It is essential for a business organization to get the customer feedback in order to grow as a company. Business organizations are collecting customer feedback using various methods. But the question is ‘are they efficient and effective?’ In the current context, there is more of a customer oriented market and all the business organizations are competing to achieve customer delight through their products and services. Social Media plays a huge role in one’s life. Customers tend to reveal their true opinion about certain brands on social media rather than giving routine feedback to the producers or sellers. Because of this reason, it is identified that social media can be used as a tool to analyze customer behavior. If relevant data can be gathered from the customers’ social media feeds and if these data are analyzed properly, a clear idea to the companies what customers really think about their brand can be provided.
A novel recommender for mobile telecom alert services - linkedinAsoka Korale
1. The document describes a novel recommender algorithm developed for mobile alert services that uses both user and item data to make recommendations.
2. It supplements user data like demographics and mobile usage patterns with additional attributes to cluster users and reduce variables, allowing more accurate similarity calculations.
3. The algorithm predicts customer preferences using both user-user and item-item similarity, categorizing items by customer interests to further enrich predictions, and validates its recommendations on past user ratings.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
IRJET- Virtual Business Analyst using a Progressive Web ApplicationIRJET Journal
The document proposes a virtual business analyst platform that uses progressive web applications, SEO, and SEM to help small and medium enterprises improve their brands and connect with investors and customers. The platform would provide customized designs for company applications and help improve customer relationship management. It analyzes how innovation adoption of social media technology can help small businesses formulate digital marketing strategies to become more agile organizations. The methodology discusses using the platform to help clients target specific demographics to increase conversion rates and capital yields from paid digital advertising campaigns.
1. The document discusses the information industry and how it markets information products.
2. It describes how the information industry packages and delivers information through coordinated activities to meet user needs.
3. Several key factors that affect the information industry are discussed, including assessing user needs, identifying users, marketing strategies, and applying marketing concepts.
CONTENT AND USER CLICK BASED PAGE RANKING FOR IMPROVED WEB INFORMATION RETRIEVALijcsa
Search engines today are retrieving more than a few thousand web pages for a single query, most of which
are irrelevant. Listing results according to user needs is, therefore, a very real necessity. The challenge lies
in ordering retrieved pages and presenting them to users in line with their interests. Search engines,
therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of
the user’s query by estimating (over the web) the importance of a web page. The proposed work
investigates web page ranking methods and recently-developed improvements in web page ranking.
Further, a new content-based web page rank technique is also proposed for implementation. The proposed
technique finds out how important a particular web page is by evaluating the data a user has clicked on, as
well as the contents available on these web pages. The results demonstrate the effectiveness of the proposed
page ranking technique and its efficiency.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
Structural Balance Theory Based Recommendation for Social Service PortalYogeshIJTSRD
There is enormous data present in our world. Therefore in order to access the most accurate information is becoming more difficult and complicated. As a result many relevant information gets missed which leads to much duplication of work and effort. Due to the huge search results, the user will generally have difficulty in identifying the relevant ones. To solve this problem, a recommendation system is used. A recommendation system is nothing but a filtering information system, which is used to predict the relevance of retrieved information according to the user’s needs for some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide huge records about any requested item or service. A proper recommendation system is used to separate this information result. A recommendation system can be improved further if supported with a level of trust information. That is, recommendations are prioritized according to their level of trust. Recommending appropriate needs social service to the target volunteers will become the key to ensure continuous success of social service. Today, many social service systems does not adopt any recommendation techniques. They provide advertisement or highlights request for a small commission. G. Banupriya | M. Anand "Structural Balance Theory-Based Recommendation for Social Service Portal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41216.pdf Paper URL: https://www.ijtsrd.comengineering/software-engineering/41216/structural-balance-theorybased-recommendation-for-social-service-portal/g-banupriya
It is essential for a business organization to get the customer feedback in order to grow as a company. Business organizations are collecting customer feedback using various methods. But the question is ‘are they efficient and effective?’ In the current context, there is more of a customer oriented market and all the business organizations are competing to achieve customer delight through their products and services. Social Media plays a huge role in one’s life. Customers tend to reveal their true opinion about certain brands on social media rather than giving routine feedback to the producers or sellers. Because of this reason, it is identified that social media can be used as a tool to analyze customer behavior. If relevant data can be gathered from the customers’ social media feeds and if these data are analyzed properly, a clear idea to the companies what customers really think about their brand can be provided.
A novel recommender for mobile telecom alert services - linkedinAsoka Korale
1. The document describes a novel recommender algorithm developed for mobile alert services that uses both user and item data to make recommendations.
2. It supplements user data like demographics and mobile usage patterns with additional attributes to cluster users and reduce variables, allowing more accurate similarity calculations.
3. The algorithm predicts customer preferences using both user-user and item-item similarity, categorizing items by customer interests to further enrich predictions, and validates its recommendations on past user ratings.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
IRJET- Virtual Business Analyst using a Progressive Web ApplicationIRJET Journal
The document proposes a virtual business analyst platform that uses progressive web applications, SEO, and SEM to help small and medium enterprises improve their brands and connect with investors and customers. The platform would provide customized designs for company applications and help improve customer relationship management. It analyzes how innovation adoption of social media technology can help small businesses formulate digital marketing strategies to become more agile organizations. The methodology discusses using the platform to help clients target specific demographics to increase conversion rates and capital yields from paid digital advertising campaigns.
1. The document discusses the information industry and how it markets information products.
2. It describes how the information industry packages and delivers information through coordinated activities to meet user needs.
3. Several key factors that affect the information industry are discussed, including assessing user needs, identifying users, marketing strategies, and applying marketing concepts.
CONTENT AND USER CLICK BASED PAGE RANKING FOR IMPROVED WEB INFORMATION RETRIEVALijcsa
Search engines today are retrieving more than a few thousand web pages for a single query, most of which
are irrelevant. Listing results according to user needs is, therefore, a very real necessity. The challenge lies
in ordering retrieved pages and presenting them to users in line with their interests. Search engines,
therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of
the user’s query by estimating (over the web) the importance of a web page. The proposed work
investigates web page ranking methods and recently-developed improvements in web page ranking.
Further, a new content-based web page rank technique is also proposed for implementation. The proposed
technique finds out how important a particular web page is by evaluating the data a user has clicked on, as
well as the contents available on these web pages. The results demonstrate the effectiveness of the proposed
page ranking technique and its efficiency.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
This study compared the search results of Google, Yahoo, and Bing for queries related to health literacy. It found that the number of search results or "hits" varied between search engines and query types. For the single term "health literacy", Yahoo returned the most results, followed by Google then Bing. However, for phrase searches or those using Boolean operators, the search engine with the most results differed. An analysis of the first 40 websites for the search "health literacy" found that most were commercial or non-government organizations, with fewer from educational institutions or government sources. The study concluded that librarians can help users refine searches to find the most appropriate information sources given variations in search engine results and website sponsorships.
How to build a Personalized News Recommendation PlatformTrieu Nguyen
This document discusses how to build a personalized news recommendation platform. It explains that recommendation systems are needed to retain users, increase traffic, and improve the content experience. It describes popular techniques like collaborative filtering, content-based filtering, and hybrid systems. Specifically, it outlines a case study using a USPA framework with real social news data. Key factors for a news recommendation system are discussed like novelty, user history, and location. The document also provides a simple example of building a recommendation engine with Apache Spark.
This document is a dissertation submitted to Manchester Metropolitan University examining UK online consumers' perceptions and attitudes towards search engine marketing and e-word-of-mouth in the context of Web 2.0. It begins with an abstract outlining the research topic and methodology. It then includes acknowledgments, a table of contents, and list of figures/tables. The introduction provides background on the research topic and objectives. The literature review examines the impact of the internet on marketing, characteristics of Web 2.0, and key concepts in internet marketing like search engine marketing and e-word-of-mouth. The methodology section outlines the primary and secondary research approaches.
Predicting the Brand Popularity from the Brand MetadataIJECEIAES
The document presents a framework for predicting brand popularity from brand metadata on social networks. It identifies thoughtful comments from brand posts using natural language processing and classifies them as favorable or unfavorable. Brand metadata like numbers of likes, shares, and identified thoughtful comments are then combined to forecast future brand popularity. The performance of the proposed framework is evaluated against recent works in terms of thoughtful comment identification accuracy, execution time, popularity prediction accuracy, and prediction time. Results show improvements over existing approaches.
This document summarizes a study on the effect of using social network sites for business marketing in Bahraini organizations. The study aimed to identify how useful social networks are for marketing, their effective utilization, and their relationship to profit and customer loyalty. A literature review covered the history and characteristics of social networks. The researcher conducted a survey of 65 Bahraini businesses and analyzed the results. The study found that social network marketing can increase awareness and loyalty if used positively, but not reputation. It also found a strong correlation between social media use and increased inquiries, orders, revenue, market share and profit.
Search Analytics For Content Strategists @CSofNYCWIKOLO
Search is a conversation, learn to listen to what you visitors are telling you by understanding their search behavior. In this presentation we'll cover information foraging, search analysis, and how to use them and other techniques to improve your content without having to be a statistician.
Can social media marketing-improve-customer-relationship 2017-journal-of-intadnan haidar
This document discusses a study examining how social media usage can help firms build new customer relationship management (CRM) capabilities and improve marketing strategies and business performance. The study uses dynamic capabilities theory and the resource-based view as frameworks. It hypothesizes that social CRM capabilities, defined as a firm's ability to generate, integrate, and respond to customer information from social media interactions, will be positively associated with customer engagement and firm performance, and that social media usage moderates the relationship between social CRM capabilities and firm performance. The study analyzes data from 232 companies to test these hypotheses.
Service Rating Prediction by check-in and check-out behavior of user and POIIRJET Journal
This document proposes a system to predict service ratings by analyzing users' check-in and check-out behaviors and points of interest (POI). It aims to mine relationships between user ratings and geographical distances between users/items. The system would integrate user-item geographical connections, user-user geographical connections, and interest similarities into a location-based rating prediction model. It was found that users often give higher ratings to items farther away from their activity centers. Users and their geographically distant friends also often give similar ratings. The proposed model is evaluated on a Yelp dataset and shows improved performance over existing approaches.
Keyword Based Service Recommendation system for Hotel System using Collaborat...IRJET Journal
This document presents a keyword-based service recommendation system using collaborative filtering to address challenges with traditional recommender systems when dealing with large datasets. The proposed system captures user preferences through keywords selected from a candidate list. It identifies similar users based on keyword similarities in preferences. It then calculates personalized ratings for services for a given user and generates a personalized recommendation list. The system aims to provide more accurate and scalable recommendations compared to existing approaches by incorporating keywords to represent user preferences.
An approach for evaluation of social...STIinnsbruck
This document describes an evaluation framework for social media monitoring tools. It proposes criteria to analyze these tools from three perspectives: concepts implemented, technologies used, and user interface. Concepts include analysis, insights, engagement, workflow management, and influence. Technologies include listening grid adjustment, near real-time processing, API integration, sentiment analysis, and access to historical data. The user interface is also important and should include a customizable dashboard and ability to export results. The evaluation framework is intended to help enterprises choose the right monitoring tool for their needs.
The study is to analyse the digital marketing techniques used by the customers with respect to construction industry. The study is done in order to gain knowledge about the utility of digital marketing in construction industry and to identify how effectively it can be used. The researcher circulated the questionnaire among the general public. The most preferred digital marketing technique in construction industry is found to be social media marketing. In future marketing agents of construction industry need to develop advertisements to be posted in social media websites.
This document provides a literature review of research on consumer behavior in social commerce. It begins by defining social commerce and discussing the different types. It then describes the systematic process used to identify and collect relevant academic articles for review. These articles are classified and their key findings are summarized. Specifically, important theories, research contexts, and methods are discussed. Finally, an integrative framework is proposed based on the Stimulus-Organism-Response model and consumer decision-making process to provide a foundation for future social commerce research.
Social Targeting: Understanding Social Media Data Mining & AnalysisInfini Graph
Chase McMichael – CEO, InfiniGraph
Social Targeting: Understanding Social Media Data Mining & Analysis
With the advent of the social web, companies that aren’t actively mining, analysing and using social media data are missing a huge commercial advantage. In this session Chase McMichael will explain how social targeting works, including technologies, techniques and opportunities. He will also highlight the privacy challenges facing the industry.
Factors affecting customer loyalty in telecom sector in indiaNicole Valerio
Hello Sir
We are a premier academic writing agency with industry partners in UK, Australia and Middle East and over 15 years of experience. We are looking to establish long-term relationships with industry partners and would love to discuss this opportunity further with you.
Thanks & Regards
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Research proposal sample|cheapassignmenthelp.comNicole Valerio
Hello Sir
We are a premier academic writing agency with industry partners in UK, Australia and Middle East and over 15 years of experience. We are looking to establish long-term relationships with industry partners and would love to discuss this opportunity further with you.
Thanks & Regards
visit our website.
www.onlineassignmenthelp.com.au
www.freeassignmenthelp.com
www.btechndassignment.cheapassignmenthelp.co.uk
www.cheapassignmenthelp.com
www.cheapassignmenthelp.co.uk/
http://www.cheapassignmenthelp.net/
This document presents a framework for analyzing digital marketing research. It identifies touchpoints in the marketing process and strategy where digital technologies have significantly impacted or will impact. The authors review existing research organized by the framework's elements and touchpoints. They outline evolving issues and questions for future research. Finally, they propose a research agenda to examine these questions from the firm's perspective, focusing on strategic, tactical and implementation implications.
Social Media Marketing Research Companies16ramanan
SMM research companies use netnography techniques to provide useful insights for understanding online consumer groups. Netnography is a naturalistic, immersive, descriptive, and multi-method approach to online marketing research that is faster, simpler, and less expensive than traditional methods. It focuses on cultural insights and pays close attention to context. SMM research firms offer online surveys, interviews, and focus groups to draw conclusions about online communities and understand expressed attitudes, meanings, and consumption patterns.
3: Reliability analysis of variables
Variables No. of Items Cronbach's Alpha
Use of social media 7 0.872
Trust 4 0.849
Retention 3 0.791
1) The study examined relationships between use of social media, trust, and customer retention in the hotel industry.
2) A survey found positive correlations between the three variables, suggesting that greater use of social media builds customer trust and retention.
3) Limitations included a focus on metropolitan Malaysia and reliance on self-reported experiences rather than measured behaviors. Future research could address limitations and further analyze relationships.
This document provides an overview of recommender systems, including:
- Recommender systems use historical user data and ratings to predict items users will like and provide personalized recommendations.
- They can increase sales, customer satisfaction and loyalty by helping users find relevant items and reducing search time.
- Common algorithms include item-item, user-user, and model blending, each comparing users or items to find similarities.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
Recommender systems provide suggestions for items to users based on their preferences. They analyze data on items, users, and transactions between users and items. Common data sources include item metadata, user profiles and ratings, and records of users' purchases or ratings of items. Recommender systems aim to provide personalized recommendations to increase sales, suggest diverse items, improve user satisfaction and loyalty, and help users find relevant items. Collaborative filtering analyzes similarities between users to provide recommendations for items liked by similar users.
This study compared the search results of Google, Yahoo, and Bing for queries related to health literacy. It found that the number of search results or "hits" varied between search engines and query types. For the single term "health literacy", Yahoo returned the most results, followed by Google then Bing. However, for phrase searches or those using Boolean operators, the search engine with the most results differed. An analysis of the first 40 websites for the search "health literacy" found that most were commercial or non-government organizations, with fewer from educational institutions or government sources. The study concluded that librarians can help users refine searches to find the most appropriate information sources given variations in search engine results and website sponsorships.
How to build a Personalized News Recommendation PlatformTrieu Nguyen
This document discusses how to build a personalized news recommendation platform. It explains that recommendation systems are needed to retain users, increase traffic, and improve the content experience. It describes popular techniques like collaborative filtering, content-based filtering, and hybrid systems. Specifically, it outlines a case study using a USPA framework with real social news data. Key factors for a news recommendation system are discussed like novelty, user history, and location. The document also provides a simple example of building a recommendation engine with Apache Spark.
This document is a dissertation submitted to Manchester Metropolitan University examining UK online consumers' perceptions and attitudes towards search engine marketing and e-word-of-mouth in the context of Web 2.0. It begins with an abstract outlining the research topic and methodology. It then includes acknowledgments, a table of contents, and list of figures/tables. The introduction provides background on the research topic and objectives. The literature review examines the impact of the internet on marketing, characteristics of Web 2.0, and key concepts in internet marketing like search engine marketing and e-word-of-mouth. The methodology section outlines the primary and secondary research approaches.
Predicting the Brand Popularity from the Brand MetadataIJECEIAES
The document presents a framework for predicting brand popularity from brand metadata on social networks. It identifies thoughtful comments from brand posts using natural language processing and classifies them as favorable or unfavorable. Brand metadata like numbers of likes, shares, and identified thoughtful comments are then combined to forecast future brand popularity. The performance of the proposed framework is evaluated against recent works in terms of thoughtful comment identification accuracy, execution time, popularity prediction accuracy, and prediction time. Results show improvements over existing approaches.
This document summarizes a study on the effect of using social network sites for business marketing in Bahraini organizations. The study aimed to identify how useful social networks are for marketing, their effective utilization, and their relationship to profit and customer loyalty. A literature review covered the history and characteristics of social networks. The researcher conducted a survey of 65 Bahraini businesses and analyzed the results. The study found that social network marketing can increase awareness and loyalty if used positively, but not reputation. It also found a strong correlation between social media use and increased inquiries, orders, revenue, market share and profit.
Search Analytics For Content Strategists @CSofNYCWIKOLO
Search is a conversation, learn to listen to what you visitors are telling you by understanding their search behavior. In this presentation we'll cover information foraging, search analysis, and how to use them and other techniques to improve your content without having to be a statistician.
Can social media marketing-improve-customer-relationship 2017-journal-of-intadnan haidar
This document discusses a study examining how social media usage can help firms build new customer relationship management (CRM) capabilities and improve marketing strategies and business performance. The study uses dynamic capabilities theory and the resource-based view as frameworks. It hypothesizes that social CRM capabilities, defined as a firm's ability to generate, integrate, and respond to customer information from social media interactions, will be positively associated with customer engagement and firm performance, and that social media usage moderates the relationship between social CRM capabilities and firm performance. The study analyzes data from 232 companies to test these hypotheses.
Service Rating Prediction by check-in and check-out behavior of user and POIIRJET Journal
This document proposes a system to predict service ratings by analyzing users' check-in and check-out behaviors and points of interest (POI). It aims to mine relationships between user ratings and geographical distances between users/items. The system would integrate user-item geographical connections, user-user geographical connections, and interest similarities into a location-based rating prediction model. It was found that users often give higher ratings to items farther away from their activity centers. Users and their geographically distant friends also often give similar ratings. The proposed model is evaluated on a Yelp dataset and shows improved performance over existing approaches.
Keyword Based Service Recommendation system for Hotel System using Collaborat...IRJET Journal
This document presents a keyword-based service recommendation system using collaborative filtering to address challenges with traditional recommender systems when dealing with large datasets. The proposed system captures user preferences through keywords selected from a candidate list. It identifies similar users based on keyword similarities in preferences. It then calculates personalized ratings for services for a given user and generates a personalized recommendation list. The system aims to provide more accurate and scalable recommendations compared to existing approaches by incorporating keywords to represent user preferences.
An approach for evaluation of social...STIinnsbruck
This document describes an evaluation framework for social media monitoring tools. It proposes criteria to analyze these tools from three perspectives: concepts implemented, technologies used, and user interface. Concepts include analysis, insights, engagement, workflow management, and influence. Technologies include listening grid adjustment, near real-time processing, API integration, sentiment analysis, and access to historical data. The user interface is also important and should include a customizable dashboard and ability to export results. The evaluation framework is intended to help enterprises choose the right monitoring tool for their needs.
The study is to analyse the digital marketing techniques used by the customers with respect to construction industry. The study is done in order to gain knowledge about the utility of digital marketing in construction industry and to identify how effectively it can be used. The researcher circulated the questionnaire among the general public. The most preferred digital marketing technique in construction industry is found to be social media marketing. In future marketing agents of construction industry need to develop advertisements to be posted in social media websites.
This document provides a literature review of research on consumer behavior in social commerce. It begins by defining social commerce and discussing the different types. It then describes the systematic process used to identify and collect relevant academic articles for review. These articles are classified and their key findings are summarized. Specifically, important theories, research contexts, and methods are discussed. Finally, an integrative framework is proposed based on the Stimulus-Organism-Response model and consumer decision-making process to provide a foundation for future social commerce research.
Social Targeting: Understanding Social Media Data Mining & AnalysisInfini Graph
Chase McMichael – CEO, InfiniGraph
Social Targeting: Understanding Social Media Data Mining & Analysis
With the advent of the social web, companies that aren’t actively mining, analysing and using social media data are missing a huge commercial advantage. In this session Chase McMichael will explain how social targeting works, including technologies, techniques and opportunities. He will also highlight the privacy challenges facing the industry.
Factors affecting customer loyalty in telecom sector in indiaNicole Valerio
Hello Sir
We are a premier academic writing agency with industry partners in UK, Australia and Middle East and over 15 years of experience. We are looking to establish long-term relationships with industry partners and would love to discuss this opportunity further with you.
Thanks & Regards
visit our website.
www.onlineassignmenthelp.com.au
www.freeassignmenthelp.com
www.btechndassignment.cheapassignmenthelp.co.uk
www.cheapassignmenthelp.com
www.cheapassignmenthelp.co.uk/
http://www.cheapassignmenthelp.net/
Research proposal sample|cheapassignmenthelp.comNicole Valerio
Hello Sir
We are a premier academic writing agency with industry partners in UK, Australia and Middle East and over 15 years of experience. We are looking to establish long-term relationships with industry partners and would love to discuss this opportunity further with you.
Thanks & Regards
visit our website.
www.onlineassignmenthelp.com.au
www.freeassignmenthelp.com
www.btechndassignment.cheapassignmenthelp.co.uk
www.cheapassignmenthelp.com
www.cheapassignmenthelp.co.uk/
http://www.cheapassignmenthelp.net/
This document presents a framework for analyzing digital marketing research. It identifies touchpoints in the marketing process and strategy where digital technologies have significantly impacted or will impact. The authors review existing research organized by the framework's elements and touchpoints. They outline evolving issues and questions for future research. Finally, they propose a research agenda to examine these questions from the firm's perspective, focusing on strategic, tactical and implementation implications.
Social Media Marketing Research Companies16ramanan
SMM research companies use netnography techniques to provide useful insights for understanding online consumer groups. Netnography is a naturalistic, immersive, descriptive, and multi-method approach to online marketing research that is faster, simpler, and less expensive than traditional methods. It focuses on cultural insights and pays close attention to context. SMM research firms offer online surveys, interviews, and focus groups to draw conclusions about online communities and understand expressed attitudes, meanings, and consumption patterns.
3: Reliability analysis of variables
Variables No. of Items Cronbach's Alpha
Use of social media 7 0.872
Trust 4 0.849
Retention 3 0.791
1) The study examined relationships between use of social media, trust, and customer retention in the hotel industry.
2) A survey found positive correlations between the three variables, suggesting that greater use of social media builds customer trust and retention.
3) Limitations included a focus on metropolitan Malaysia and reliance on self-reported experiences rather than measured behaviors. Future research could address limitations and further analyze relationships.
This document provides an overview of recommender systems, including:
- Recommender systems use historical user data and ratings to predict items users will like and provide personalized recommendations.
- They can increase sales, customer satisfaction and loyalty by helping users find relevant items and reducing search time.
- Common algorithms include item-item, user-user, and model blending, each comparing users or items to find similarities.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
Recommender systems provide suggestions for items to users based on their preferences. They analyze data on items, users, and transactions between users and items. Common data sources include item metadata, user profiles and ratings, and records of users' purchases or ratings of items. Recommender systems aim to provide personalized recommendations to increase sales, suggest diverse items, improve user satisfaction and loyalty, and help users find relevant items. Collaborative filtering analyzes similarities between users to provide recommendations for items liked by similar users.
Personalized E-commerce based recommendation systems using deep-learning tech...IAESIJAI
As technology is surpassing each day, with the variation of personalized drifts
relevant to the explicit behavior of users using the internet. Recommendation
systems use predictive mechanisms like predicting a rating that a customer
could give on a specific item. This establishes a ranked list of items according
to the preferences each user makes concerning exhibiting personalized
recommendations. The existing recommendation techniques are efficient in
systematically creating recommendation techniques. This approach
encounters many challenges such as determining the accuracy, scalability, and
data sparsity. Recently deep learning attains significant research to enhance
the performance to improvise feature specification in learning the efficiency
of retrieving the necessary information as well as a recommendation system
approach. Here, we provide a thorough review of the deep-learning
mechanism focused on the learning-rates-based prediction approach modeled
to articulate the widespread summary for the state-of-art techniques. The
novel techniques ensure the incorporation of innovative perspectives to
pertain to the unique and exciting growth in this field.
A Community Detection and Recommendation SystemIRJET Journal
This document proposes a community detection and recommendation system that uses community detection algorithms to analyze social networks and extract friendship relationships between users. It will develop the approach using the MapReduce framework to improve the scalability, coverage, and cold start issues of collaborative filtering recommendation systems. The system aims to provide more accurate recommendations by incorporating social network information and trust between users into the recommendation process.
IRJET- Analysis on Existing Methodologies of User Service Rating Prediction S...IRJET Journal
This document summarizes and analyzes existing methodologies for user service rating prediction systems. It discusses recommendation systems including collaborative filtering, content-based filtering, and hybrid approaches. Collaborative filtering predicts user ratings based on opinions of other similar users but faces challenges of cold start, scalability, and sparsity. Content-based filtering relies on item profiles and user preferences to recommend similar items but requires detailed item information. Hybrid systems combine collaborative and content-based filtering to address their individual limitations. The document also examines social recommender systems and how they can account for relationship strength, expertise, and user similarity within social networks.
A Systematic Literature Survey On Recommendation SystemGina Rizzo
This document provides a literature review of recommendation systems. It discusses different recommendation models including collaborative filtering, content-based filtering, and hybrid approaches. Collaborative filtering techniques make recommendations based on the ratings and preferences of similar users, while content-based filtering relies on the characteristics of the items. The document also outlines key application areas of recommendation systems like movies, products, jobs, and friends. Overall, the review examines research trends in recommendation techniques and their use across different service industries to improve user experience and business outcomes.
with current projections regarding the growth of
Internet sales, online retailing raises many questions about how
to market on the Net. A Recommender System (RS) is a
composition of software tools that provides valuable piece of
advice for items or services chosen by a user. Recommender
systems are currently useful in both the research and in the
commercial areas. Recommender systems are a means of
personalizing a site and a solution to the customer’s information
overload problem. Recommender Systems (RS) are software
tools and techniques providing suggestions for items and/or
services to be of use to a user. These systems are achieving
widespread success in ecommerce applications now a days, with
the advent of internet. This paper presents a categorical review
of the field of recommender systems and describes the state-ofthe-
art of the recommendation methods that are usually
classified into four categories: Content based Collaborative,
Demographic and Hybrid systems. To build our recommender
system we will use fuzzy logic and Markov chain algorithm.
IRJET- A New Approach to Product Recommendation SystemsIRJET Journal
1. The document proposes a new approach to product recommendation systems for e-commerce websites that uses multiple algorithms and user verification.
2. It clusters users based on purchase history and recommends products to a user based on the purchases and ratings of similar users, while also considering a user's indicated likes and dislikes.
3. A key aspect is verifying that reviews are from actual customers by requiring users to enter a transaction ID and one-time password sent by email after purchasing a product before they can post a review. This helps reduce fake reviews.
IRJET- A New Approach to Product Recommendation SystemsIRJET Journal
This document proposes a new approach to product recommendation systems for e-commerce websites. It discusses some limitations of current recommendation systems, such as being business motivated or only based on individual user interests. The proposed system aims to find similar users based on their ratings and dislikes to make recommendations. It also implements a verification step to only allow reviews from users who have purchased the product, to ensure reviews are genuine. The system would cluster users based on interests and notify further recommendations to users in the same cluster.
System For Product Recommendation In E-Commerce ApplicationsIJERD Editor
This document summarizes a research paper that proposes a personalized hybrid recommendation system for e-commerce applications that can support massive datasets. The system uses clustering algorithms to build a user preference tree to model user interests. It then uses map-reduce on Hadoop to accelerate the recommendation algorithm using user and product similarity matrices in order to provide recommendations to users in an online mode quickly despite large, unstructured data. The performance of the map-reduce based system is analyzed and shown to have advantages over traditional centralized methods for large datasets.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
This document describes a proposed algorithm for improving recommendation systems for e-services. It involves the following key steps:
1. Clustering customer transaction histories to group similar purchase patterns and derive customer-based recommendations.
2. Using incremental association rule mining on the transaction data to detect frequently purchased item sets and relationships between items.
3. Developing a fuzzy model to classify customers and provide dynamic recommendations tailored to different customer types. The recommendations will be based on matching customer preferences and purchase histories to specific product sets.
4. The algorithm clusters transactions, mines association rules incrementally as new data is added, and generates recommendations by classifying customers and matching them to relevant product clusters. This provides a personalized and
IRJET- Hybrid Book Recommendation SystemIRJET Journal
This document describes a hybrid book recommendation system that aims to overcome some common issues with recommendation systems like the cold start problem. The system collects demographic information from users during signup to provide more personalized recommendations. It uses both collaborative and content-based filtering approaches. For new users, it recommends books based on their interests. For users without ratings, it considers their purchase history. For users who provide ratings, it uses algorithms like KNN, SVD, RBM and hybrid approaches. The system aims to improve accuracy and provide a more personalized experience for users.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
IRJET- Review on Different Recommendation Techniques for GRS in Online Social...IRJET Journal
This document reviews different recommendation techniques for group recommender systems (GRS) in online social networks. It discusses traditional recommender approaches like content-based filtering and collaborative filtering. It also reviews related work applying opinion dynamics models and weight matrices to GRS. The document concludes that using a smart weights matrix to consider relationships between group members' preferences in a recommendation process improves aggregation and ensures consensus, providing the best way to recommend items to a complete group.
IRJET- Analysis of Rating Difference and User InterestIRJET Journal
This document summarizes a research paper that proposes a collaborative filtering recommendation algorithm that incorporates rating differences and user interests. It first adds a rating difference factor to the traditional collaborative filtering algorithm. It then calculates user interests based on item attributes and the similarity between user interests. Recommendations are made by weighting user rating differences and interest similarities. The proposed algorithm is shown to reduce error rates and improve accuracy compared to traditional collaborative filtering.
Mixed Recommendation Algorithm Based on Content, Demographic and Collaborativ...IRJET Journal
The document describes a proposed hybrid recommendation algorithm that incorporates content filtering, collaborative filtering, and demographic filtering. It begins with an overview of recommendation systems and different filtering techniques. Then, it discusses related work incorporating various filtering approaches. The methodology section outlines the original algorithm, which develops user profiles based on browsing history and ratings. It provides recommendations by calculating similarities between user and item profiles. The proposed methodology enhances this by incorporating demographic attributes into user profiles and using fuzzy logic to validate recommendations. It claims this integrated approach can provide more accurate and personalized recommendations.
A NOVEL RESEARCH PAPER RECOMMENDATION SYSTEMKarla Adamson
This document presents a novel research paper recommendation system using collaborative filtering approaches. It proposes both user-based and item-based collaborative filtering to implement a recommender system. Users create profiles with their details and interests. The system then recommends different research papers to users based on similarities between the user's profile and other users' profiles (user-based) or similarities between items the user has interacted with (item-based). Four algorithms are implemented in each category and evaluated based on recommendation accuracy metrics like precision and recall. The goal is to help researchers find relevant papers more efficiently by leveraging patterns in user interests and behaviors.
Research often spend a considerable amount of time searching for published papers and articles relevant to their interest, dissertation and research work. A recommender engine is a tool, a means to answer the question. “What are the best recommendations for a user?” Using trust in social networks provides a promising approach to make recommendations to other user based on trust propagation in finding research papers or research papers of a friend/research with similar interests. However, current recommendation algorithms are based on user-item rating. A collaborative filtering based research paper recommender system is proposed here with User and Item Based collaborative filtering approach to implement a recommender system for Research Paper.
Personalized recommendation for cold start usersIRJET Journal
The document discusses personalized recommendation methods for cold start users. It describes several recommendation techniques including content-based filtering, collaborative filtering, and hybrid recommendation. It also discusses challenges like cold start problems and data sparsity. Trust-based recommendation systems are described that incorporate social relationships between users. Matrix factorization techniques are discussed for modeling user-item interactions and incorporating additional contextual factors. The use of probabilistic matrix factorization models to address cold start and sparsity problems is also covered.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
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1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
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STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
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Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
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A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
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This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
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Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
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Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
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